Past Meetup

Keep Current :: NLP Seminar #1 - Dimension Reduction

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WeAreDeveloper Office

Doblhoffgasse 9 Tür 14 · Wien

How to find us

Pay attention: use the Doblhoffgasse 9 entrance, with the sign: WeAreDevelopers - Reception. There's a buzzer on the right side of the main door. We're on the 4th floor, door 14. The meeting room is on the left.

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Details

Machine Learning Seminar #1 - Dimension Reduction in Natural Language Processing

Level: Advanced

This is the first event in a series of seminars for approaching, understanding and working with Machine learning from different perspectives. These events are not a lectures, but rather discussions, with the aim of learning from each other perspective.

This event will focus on dimension reduction aspects in the field of Natural Language Processing.

We will discuss and explore similarities and differences across varying methodologies - from PCA and LDA to GloVe and FastText - in an attempt to understand better the best uses and limitations of these tools.

The seminar format works best if you come prepared. Please check the reading list below and bring your own insights, questions, and perplexities to the table!

This is our recommended reading list:

LDA
http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

T-SNE
https://www.youtube.com/watch?v=RJVL80Gg3lA

PCA
https://medium.com/@aptrishu/understanding-principle-component-analysis-e32be0253ef0
http://setosa.io/ev/principal-component-analysis/

Word2vec
- https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf
- https://arxiv.org/abs/1402.3722
- https://levyomer.files.wordpress.com/2014/09/neural-word-embeddings-as-implicit-matrix-factorization.pdf

- https://www.tensorflow.org/tutorials/representation/word2vec
- https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c

GloVe
https://nlp.stanford.edu/pubs/glove.pdf

TECHNIQUES COMPARISSON
https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/
https://arxiv.org/ftp/arxiv/papers/1403/1403.2877.pdf

Please feel free to add more sources in the comments.

We look forward to seeing you!